Deep learning identified glioblastoma subtypes based on internal genomic expression ranks.

ABSTRACT:

Background

Glioblastoma (GBM) can be divided into subtypes according to their genomic features, including Proneural (PN), Neural (NE), Classical (CL) and Mesenchymal (ME). However, it is a difficult task to unify various genomic expression profiles which were standardized with various procedures from different studies and to manually classify a given GBM sample into a subtype.

Methods

An algorithm was developed to unify the genomic profiles of GBM samples into a standardized normal distribution (SND), based on their internal expression ranks. Deep neural networks (DNN) and convolutional DNN (CDNN) models were trained on original and SND data. In addition, expanded SND data by combining various The Cancer Genome Atlas (TCGA) datasets were used to improve the robustness and ge

SUBMITTER: Mao XG 

PROVIDER: S-EPMC8780813 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature